Shear Strength of Reinforced Concrete Beams Made with RecycledAggregate
Bibliographic record
Abstract
Using recycled concrete aggregate in new concrete mixes saves landfill space, reduces the need for gravel mining, and decreases pollution from transporting concrete to and from worksites. Recycled concrete aggregate may come from a variety of sources but the most common source is the demolition waste of old concrete structures. In this study, the recycled aggregate was obtained locally from Bee'ah's facility in Sharjah, UAE, and the percentage of coarse aggregate replacement in the concrete mix was either 50% or 100%, in addition to the control mix which included 100% natural aggregate. The target 28-day compressive strength based on 150mmx300mm cylinder was within the range 30-35MPa. The first phase of the study addressed the recycled aggregate tests and concrete mix design while the second phase involved shear strength of three half-scale reinforced concrete beams made with the recycled and natural aggregate. In the later part, the simply supported beams were tested under a single-point loading scheme inside a Universal Test Machine at a shear span-to-depth ratio of 1.5 to determine the load-deflection performance up to failure. Results of the study showed that the elastic stiffness, ultimate shear capacity and residual strength of steel reinforced beams made with 50% and 100% recycled coarse aggregate were comparable to those made entirely with natural aggregate and can be conservatively predicted by the theoretical equations of the ACI 318 code.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".